In some cases, we might want to detect outlier and change-point simultaneously in order to figure out characteristics of a time series both in a local and global scale. ChangeFinder is an anomaly detection technique which enables us to detect both of outliers and change-points in a single framework. A key reference for the technique is:

Detecting outliers and change-points of the 5-dimensional data

Using changefinder() for multi-dimensional data requires us to pass the first argument as an array. In our case, the data is 5-dimensional, so the first argument should be an array with 5 elements. Except for that point, basic usage of the function is same as the previous 1-dimensional example: